Purpose Coronary artery disease (CAD) is one of the most significant cardiovascular diseases that requires accurate angiography to diagnose. Angiography is an invasive approach involving risks like death, heart attack, and stroke. An appropriate alternative for diagnosis of the disease is to use statistical or data mining methods. The purpose of the study was to predict CAD by using discriminant analysis and compared with the logistic regression. Materials and methods This cross-sectional study included 758 cases admitted to Fatemeh Zahra Teaching Hospital (Sari, Iran) for examination and coronary angiography for evaluation of CAD in 2019. A logistics discriminant, Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) model and K-Nearest Neighbor (KNN) were fitted for prognosis of CAD with the help of clinical and laboratory information of patients. Results Out of the 758 examined cases, 250 (32.98%) cases were non-CAD and 508 (67.22%) were diagnosed with CAD disease. The results indicated that the indices of accuracy, sensitivity, specificity and area under the ROC curve (AUC) in the linear discriminant analysis (LDA) were 78.6, 81.3, 71.3, and 81.9%, respectively. The results obtained by the quadratic discriminant analysis were respectively 64.6, 88.2, 47.9, and 81%. The values of the metrics in K-nearest neighbor method were 74, 77.5, 63.7, and 82%, respectively. Finally, the logistic regression reached 77, 87.6, 55.6, and 82%, respectively for the evaluation metrics. Conclusions The LDA method is superior to the Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN) and Logistic Regression (LR) methods in differentiating CAD patients. Therefore, in addition to common non-invasive diagnostic methods, LDA technique is recommended as a predictive model with acceptable accuracy, sensitivity, and specificity for the diagnosis of CAD. However, given that the differences between the models are small, it is recommended to use each model to predict CAD disease.

Background Coronary artery disease (CAD) is considered as an inflammatory disease. Cardiovascular disease (CVD) is a major cause of death and disability worldwide. This study aimed to compare the performance of different non-invasive CAD diagnostic techniques. Methods A cross-sectional study was performed on a total of 758 subjects (250 with CAD and 508 without CAD). We compared the performances of logistic regression (LR) model, artificial neural networks (ANN), and support vector machines (SVMs) for the purpose of functioning. The Performance of classification techniques were compared using ROC curve, sensitivity, specificity, and accuracy. Results The study population consisted of 758 case subjects. Two hundred fifty of them (33.6% men and 66.4% women) were eventually diagnosed with non-CAD, while 508 subjects (64% men and 36% women) were not (33.6% men and 66.4% women). The area under the ROC Curve (AUC) for CAD resulted in 0.775 (95% CI: 0.711, 0.838) for Logistic regression model, 0.752 (95% CI: 0.682, 0.823) for ANN, and 0.793 (95% CI: 0.733, 0.853) for SVMs, respectively. There were significant differences between these three models in prediction of CAD (p = 0.04). The best model of forecasting CAD was the SVMs (0.793, 95% CI: 0.733, 0.853). However, the differences between logistic regression model, ANN and LR with SVMs were small and non-significant (p = 0.2, p = 0.09). Conclusions Support vector machines (SVMs) yielded better performance than ANN model to predict the risk of coronary artery disease (CAD) with simple clinical predictors. However, support vector machines produced as much performance as the LR model.